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Abstract Details
Activity Number:
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123
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Type:
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Contributed
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Date/Time:
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Monday, August 1, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract - #303094 |
Title:
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Multiset Model Selection
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Author(s):
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Dipayan Maiti*+ and Scotland Charles Leman
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Companies:
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Virginia Tech and Virginia Tech
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Address:
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Department of Statistics, Blacksburg, VA, 24060, United States
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Keywords:
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Multiset ;
Bayesian Model Selection ;
Bayesian Model Averaging
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Abstract:
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The Multiset Sampler has previously been deployed and developed for e?cient sampling from complex stochastic processes. We extend the sampler and the surrounding theory to high dimensional model selection problems. In such problems e?cient exploration of the model space becomes a challenge since independent and ad-hoc proposals might not be able to jointly propose multiple parameter sets which correctly explain a new proposed model. In order to overcome this we propose a multiset on the model space to enable e?cient exploration of multiple model modes. The model selection framework is based on independent priors for the parameters and model indicators on variables. While under this method we do not obtain typical Bayesian model averaged estimates for the parameters, we show that the multiset model averaged parameter estimate is a mixture a distribution from which the true Bayesian model probabilities and the model averaged parameter estimate can be obtained.
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